{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 导入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_table('./log.txt', sep = '\\t', header = None, names = ['id','api', 'count','res_time_sum', 'res_time_min', 'res_time_max', 'res_time_avg','interval','created_at'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>api</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019162542</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162644</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162742</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162808</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162943</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179491</th>\n",
       "      <td>13438800</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>11</td>\n",
       "      <td>2783.48</td>\n",
       "      <td>99.24</td>\n",
       "      <td>489.90</td>\n",
       "      <td>253.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 23:06:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179492</th>\n",
       "      <td>13438866</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>10</td>\n",
       "      <td>1951.10</td>\n",
       "      <td>85.37</td>\n",
       "      <td>529.51</td>\n",
       "      <td>195.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 23:07:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179493</th>\n",
       "      <td>13438917</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>494.17</td>\n",
       "      <td>103.95</td>\n",
       "      <td>211.47</td>\n",
       "      <td>164.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 23:08:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179494</th>\n",
       "      <td>13438981</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>9</td>\n",
       "      <td>1798.28</td>\n",
       "      <td>101.11</td>\n",
       "      <td>433.30</td>\n",
       "      <td>199.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 23:09:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179495</th>\n",
       "      <td>13439086</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>6</td>\n",
       "      <td>1017.97</td>\n",
       "      <td>74.45</td>\n",
       "      <td>298.97</td>\n",
       "      <td>169.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 23:10:21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>179496 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                id                     api  count  res_time_sum  res_time_min  \\\n",
       "0       2019162542  /front-api/bill/create      8       1057.31         88.75   \n",
       "1           162644  /front-api/bill/create      5        749.12        103.79   \n",
       "2           162742  /front-api/bill/create      5        845.84        136.31   \n",
       "3           162808  /front-api/bill/create      9       1305.52         90.12   \n",
       "4           162943  /front-api/bill/create      3        568.89        138.45   \n",
       "...            ...                     ...    ...           ...           ...   \n",
       "179491    13438800  /front-api/bill/create     11       2783.48         99.24   \n",
       "179492    13438866  /front-api/bill/create     10       1951.10         85.37   \n",
       "179493    13438917  /front-api/bill/create      3        494.17        103.95   \n",
       "179494    13438981  /front-api/bill/create      9       1798.28        101.11   \n",
       "179495    13439086  /front-api/bill/create      6       1017.97         74.45   \n",
       "\n",
       "        res_time_max  res_time_avg  interval           created_at  \n",
       "0             177.72         132.0        60  2018-11-01 00:00:07  \n",
       "1             240.38         149.0        60  2018-11-01 00:01:07  \n",
       "2             225.73         169.0        60  2018-11-01 00:02:07  \n",
       "3             196.61         145.0        60  2018-11-01 00:03:07  \n",
       "4             232.02         189.0        60  2018-11-01 00:04:07  \n",
       "...              ...           ...       ...                  ...  \n",
       "179491        489.90         253.0        60  2019-05-30 23:06:21  \n",
       "179492        529.51         195.0        60  2019-05-30 23:07:21  \n",
       "179493        211.47         164.0        60  2019-05-30 23:08:21  \n",
       "179494        433.30         199.0        60  2019-05-30 23:09:21  \n",
       "179495        298.97         169.0        60  2019-05-30 23:10:21  \n",
       "\n",
       "[179496 rows x 9 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 检测是否有异常值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 179496 entries, 0 to 179495\n",
      "Data columns (total 9 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   id            179496 non-null  int64  \n",
      " 1   api           179496 non-null  object \n",
      " 2   count         179496 non-null  int64  \n",
      " 3   res_time_sum  179496 non-null  float64\n",
      " 4   res_time_min  179496 non-null  float64\n",
      " 5   res_time_max  179496 non-null  float64\n",
      " 6   res_time_avg  179496 non-null  float64\n",
      " 7   interval      179496 non-null  int64  \n",
      " 8   created_at    179496 non-null  object \n",
      "dtypes: float64(4), int64(3), object(2)\n",
      "memory usage: 12.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                     179496\n",
       "unique                         1\n",
       "top       /front-api/bill/create\n",
       "freq                      179496\n",
       "Name: api, dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.api.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    179496.0\n",
       "mean         60.0\n",
       "std           0.0\n",
       "min          60.0\n",
       "25%          60.0\n",
       "50%          60.0\n",
       "75%          60.0\n",
       "max          60.0\n",
       "Name: interval, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.interval.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### api与interval两列的数据对所有数据点都一样，对分析无用，可以丢弃"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>2018-11-01 00:00:07</th>\n",
       "      <td>2019162542</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:01:07</th>\n",
       "      <td>162644</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:02:07</th>\n",
       "      <td>162742</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:03:07</th>\n",
       "      <td>162808</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:04:07</th>\n",
       "      <td>162943</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2019-05-30 23:06:21</th>\n",
       "      <td>13438800</td>\n",
       "      <td>11</td>\n",
       "      <td>2783.48</td>\n",
       "      <td>99.24</td>\n",
       "      <td>489.90</td>\n",
       "      <td>253.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 23:07:21</th>\n",
       "      <td>13438866</td>\n",
       "      <td>10</td>\n",
       "      <td>1951.10</td>\n",
       "      <td>85.37</td>\n",
       "      <td>529.51</td>\n",
       "      <td>195.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 23:08:21</th>\n",
       "      <td>13438917</td>\n",
       "      <td>3</td>\n",
       "      <td>494.17</td>\n",
       "      <td>103.95</td>\n",
       "      <td>211.47</td>\n",
       "      <td>164.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 23:09:21</th>\n",
       "      <td>13438981</td>\n",
       "      <td>9</td>\n",
       "      <td>1798.28</td>\n",
       "      <td>101.11</td>\n",
       "      <td>433.30</td>\n",
       "      <td>199.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 23:10:21</th>\n",
       "      <td>13439086</td>\n",
       "      <td>6</td>\n",
       "      <td>1017.97</td>\n",
       "      <td>74.45</td>\n",
       "      <td>298.97</td>\n",
       "      <td>169.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>179496 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             id  count  res_time_sum  res_time_min  \\\n",
       "created_at                                                           \n",
       "2018-11-01 00:00:07  2019162542      8       1057.31         88.75   \n",
       "2018-11-01 00:01:07      162644      5        749.12        103.79   \n",
       "2018-11-01 00:02:07      162742      5        845.84        136.31   \n",
       "2018-11-01 00:03:07      162808      9       1305.52         90.12   \n",
       "2018-11-01 00:04:07      162943      3        568.89        138.45   \n",
       "...                         ...    ...           ...           ...   \n",
       "2019-05-30 23:06:21    13438800     11       2783.48         99.24   \n",
       "2019-05-30 23:07:21    13438866     10       1951.10         85.37   \n",
       "2019-05-30 23:08:21    13438917      3        494.17        103.95   \n",
       "2019-05-30 23:09:21    13438981      9       1798.28        101.11   \n",
       "2019-05-30 23:10:21    13439086      6       1017.97         74.45   \n",
       "\n",
       "                     res_time_max  res_time_avg  \n",
       "created_at                                       \n",
       "2018-11-01 00:00:07        177.72         132.0  \n",
       "2018-11-01 00:01:07        240.38         149.0  \n",
       "2018-11-01 00:02:07        225.73         169.0  \n",
       "2018-11-01 00:03:07        196.61         145.0  \n",
       "2018-11-01 00:04:07        232.02         189.0  \n",
       "...                           ...           ...  \n",
       "2019-05-30 23:06:21        489.90         253.0  \n",
       "2019-05-30 23:07:21        529.51         195.0  \n",
       "2019-05-30 23:08:21        211.47         164.0  \n",
       "2019-05-30 23:09:21        433.30         199.0  \n",
       "2019-05-30 23:10:21        298.97         169.0  \n",
       "\n",
       "[179496 rows x 6 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df.drop(['api','interval'], axis = 1)\n",
    "df1.index = pd.to_datetime(df1.created_at)\n",
    "df1 = df1.drop('created_at',axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 取一天内的数据，并重新采样"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-04 00:00:00</th>\n",
       "      <td>22552688</td>\n",
       "      <td>23</td>\n",
       "      <td>3325.16</td>\n",
       "      <td>464.95</td>\n",
       "      <td>997.72</td>\n",
       "      <td>710.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04 00:05:00</th>\n",
       "      <td>18043425</td>\n",
       "      <td>17</td>\n",
       "      <td>2623.52</td>\n",
       "      <td>326.20</td>\n",
       "      <td>1078.94</td>\n",
       "      <td>569.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04 00:10:00</th>\n",
       "      <td>22555704</td>\n",
       "      <td>13</td>\n",
       "      <td>1826.81</td>\n",
       "      <td>548.84</td>\n",
       "      <td>908.75</td>\n",
       "      <td>729.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04 00:15:00</th>\n",
       "      <td>22557150</td>\n",
       "      <td>14</td>\n",
       "      <td>2322.69</td>\n",
       "      <td>590.66</td>\n",
       "      <td>1102.96</td>\n",
       "      <td>826.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04 00:20:00</th>\n",
       "      <td>18046725</td>\n",
       "      <td>8</td>\n",
       "      <td>1189.02</td>\n",
       "      <td>595.11</td>\n",
       "      <td>700.77</td>\n",
       "      <td>634.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04 23:35:00</th>\n",
       "      <td>22813585</td>\n",
       "      <td>32</td>\n",
       "      <td>4822.46</td>\n",
       "      <td>447.82</td>\n",
       "      <td>1207.41</td>\n",
       "      <td>742.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04 23:40:00</th>\n",
       "      <td>22815101</td>\n",
       "      <td>17</td>\n",
       "      <td>2379.47</td>\n",
       "      <td>435.84</td>\n",
       "      <td>961.44</td>\n",
       "      <td>662.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04 23:45:00</th>\n",
       "      <td>22816617</td>\n",
       "      <td>15</td>\n",
       "      <td>2215.69</td>\n",
       "      <td>504.14</td>\n",
       "      <td>939.40</td>\n",
       "      <td>710.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04 23:50:00</th>\n",
       "      <td>22818051</td>\n",
       "      <td>15</td>\n",
       "      <td>2278.75</td>\n",
       "      <td>553.72</td>\n",
       "      <td>1023.70</td>\n",
       "      <td>763.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04 23:55:00</th>\n",
       "      <td>18255708</td>\n",
       "      <td>11</td>\n",
       "      <td>1987.19</td>\n",
       "      <td>481.68</td>\n",
       "      <td>995.14</td>\n",
       "      <td>728.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>288 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           id  count  res_time_sum  res_time_min  \\\n",
       "created_at                                                         \n",
       "2019-01-04 00:00:00  22552688     23       3325.16        464.95   \n",
       "2019-01-04 00:05:00  18043425     17       2623.52        326.20   \n",
       "2019-01-04 00:10:00  22555704     13       1826.81        548.84   \n",
       "2019-01-04 00:15:00  22557150     14       2322.69        590.66   \n",
       "2019-01-04 00:20:00  18046725      8       1189.02        595.11   \n",
       "...                       ...    ...           ...           ...   \n",
       "2019-01-04 23:35:00  22813585     32       4822.46        447.82   \n",
       "2019-01-04 23:40:00  22815101     17       2379.47        435.84   \n",
       "2019-01-04 23:45:00  22816617     15       2215.69        504.14   \n",
       "2019-01-04 23:50:00  22818051     15       2278.75        553.72   \n",
       "2019-01-04 23:55:00  18255708     11       1987.19        481.68   \n",
       "\n",
       "                     res_time_max  res_time_avg  \n",
       "created_at                                       \n",
       "2019-01-04 00:00:00        997.72         710.0  \n",
       "2019-01-04 00:05:00       1078.94         569.0  \n",
       "2019-01-04 00:10:00        908.75         729.0  \n",
       "2019-01-04 00:15:00       1102.96         826.0  \n",
       "2019-01-04 00:20:00        700.77         634.0  \n",
       "...                           ...           ...  \n",
       "2019-01-04 23:35:00       1207.41         742.0  \n",
       "2019-01-04 23:40:00        961.44         662.0  \n",
       "2019-01-04 23:45:00        939.40         710.0  \n",
       "2019-01-04 23:50:00       1023.70         763.0  \n",
       "2019-01-04 23:55:00        995.14         728.0  \n",
       "\n",
       "[288 rows x 6 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_day = df1['2019-01-04'].resample('5T').sum()\n",
    "one_day"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 画出一天中不同时段的访问次数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "one_day['count'].plot(kind = 'line', color = 'blue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 画出一天中不同时段的响应时间"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "one_day[['res_time_min','res_time_max','res_time_avg']].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 画出其中15天内，不同时段访问次数的曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([737060., 737062., 737064., 737066., 737068., 737070., 737072.,\n",
       "        737074.]),\n",
       " <a list of 8 Text major ticklabel objects>)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2 = df1['2019-01-01':'2019-01-15'].resample('5T').sum()\n",
    "plt.plot(df2['count'])\n",
    "plt.xticks(rotation = 45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 分析周末和平日，不同时间段访问次数的对比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = df1.resample('H').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3['weekend'] = df3.index.weekday.isin({5,6})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df4 = df3.groupby(['weekend',df3.index.hour])['count'].mean().unstack(level = 0)\n",
    "df4.columns = ['weekdays', 'weekends']\n",
    "df4.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
